Overview

Brought to you by YData

Dataset statistics

Number of variables	38
Number of observations	180
Missing cells	40
Missing cells (%)	0.6%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	202.9 KiB
Average record size in memory	1.1 KiB

Variable types

Numeric	17
DateTime	2
Text	3
Categorical	13
Boolean	3

Alerts

`Date Time` has constant value "2025-07-03 00:00:00"	Constant
`Date` has constant value "2025-07-03 00:00:00"	Constant
`Curr_Price` is highly overall correlated with `Day1_Pred` and 9 other fields	High correlation
`Day1_Pred` is highly overall correlated with `Curr_Price` and 8 other fields	High correlation
`Day3_Pred` is highly overall correlated with `Curr_Price` and 8 other fields	High correlation
`Day7_Pred` is highly overall correlated with `Curr_Price` and 8 other fields	High correlation
`Sell` is highly overall correlated with `made_profit_act_day_1`	High correlation
`actual_day1_close` is highly overall correlated with `Curr_Price` and 9 other fields	High correlation
`actual_day3_close` is highly overall correlated with `Curr_Price` and 8 other fields	High correlation
`actual_day7_close` is highly overall correlated with `Curr_Price` and 8 other fields	High correlation
`closest_day1_price` is highly overall correlated with `Curr_Price` and 9 other fields	High correlation
`closest_day3_price` is highly overall correlated with `Curr_Price` and 8 other fields	High correlation
`closest_day7_price` is highly overall correlated with `Curr_Price` and 8 other fields	High correlation
`day1_act_error` is highly overall correlated with `day1_min_error` and 4 other fields	High correlation
`day1_min_error` is highly overall correlated with `day1_act_error` and 4 other fields	High correlation
`day3_act_error` is highly overall correlated with `day1_act_error` and 4 other fields	High correlation
`day3_min_error` is highly overall correlated with `day1_act_error` and 4 other fields	High correlation
`day7_act_error` is highly overall correlated with `day1_act_error` and 4 other fields	High correlation
`day7_min_error` is highly overall correlated with `day1_act_error` and 4 other fields	High correlation
`made_profit_act_day_1` is highly overall correlated with `Sell` and 1 other fields	High correlation
`made_profit_act_day_3` is highly overall correlated with `made_profit_act_day_7` and 3 other fields	High correlation
`made_profit_act_day_7` is highly overall correlated with `made_profit_act_day_3` and 3 other fields	High correlation
`made_profit_could_day_3` is highly overall correlated with `made_profit_act_day_3` and 3 other fields	High correlation
`made_profit_could_day_7` is highly overall correlated with `made_profit_act_day_3` and 4 other fields	High correlation
`missed_opportunity_day7` is highly overall correlated with `Curr_Price` and 2 other fields	High correlation
`profit_1` is highly overall correlated with `made_profit_act_day_1`	High correlation
`profit_3` is highly overall correlated with `made_profit_act_day_3` and 3 other fields	High correlation
`profit_7` is highly overall correlated with `made_profit_act_day_7` and 2 other fields	High correlation
`missed_opportunity_day7` is highly imbalanced (69.0%)	Imbalance
`closest_day1_price` has 5 (2.8%) missing values	Missing
`actual_day1_close` has 5 (2.8%) missing values	Missing
`day1_min_error` has 5 (2.8%) missing values	Missing
`day1_act_error` has 5 (2.8%) missing values	Missing
`closest_day3_price` has 5 (2.8%) missing values	Missing
`actual_day3_close` has 5 (2.8%) missing values	Missing
`day3_min_error` has 5 (2.8%) missing values	Missing
`day3_act_error` has 5 (2.8%) missing values	Missing
`Unnamed: 0` is uniformly distributed	Uniform
`Unnamed: 0` has unique values	Unique
`Curr_Price` has 5 (2.8%) zeros	Zeros
`day1_min_error` has 11 (6.1%) zeros	Zeros
`day3_min_error` has 2 (1.1%) zeros	Zeros

Reproduction

Analysis started	2025-07-13 14:44:31.926597
Analysis finished	2025-07-13 14:45:02.586251
Duration	30.66 seconds
Software version	ydata-profiling vv4.12.2
Download configuration	config.json

Unnamed: 0
Real number (ℝ)

Uniform Unique

Distinct	180
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	92.633333

Minimum	0
Maximum	189
Zeros	1
Zeros (%)	0.6%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0
5-th percentile	8.95
Q1	44.75
median	90.5
Q3	139.5
95-th percentile	180.05
Maximum	189
Range	189
Interquartile range (IQR)	94.75

Descriptive statistics

Standard deviation	55.571978
Coefficient of variation (CV)	0.5999134
Kurtosis	-1.2106867
Mean	92.633333
Median Absolute Deviation (MAD)	47.5
Skewness	0.068600089
Sum	16674
Variance	3088.2447
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1	0.6%
1	1	0.6%
2	1	0.6%
3	1	0.6%
4	1	0.6%
5	1	0.6%
6	1	0.6%
7	1	0.6%
8	1	0.6%
9	1	0.6%
Other values (170)	170	94.4%

Value	Count	Frequency (%)
0	1	0.6%
1	1	0.6%
2	1	0.6%
3	1	0.6%
4	1	0.6%
5	1	0.6%
6	1	0.6%
7	1	0.6%
8	1	0.6%
9	1	0.6%

Value	Count	Frequency (%)
189	1	0.6%
188	1	0.6%
187	1	0.6%
186	1	0.6%
185	1	0.6%
184	1	0.6%
183	1	0.6%
182	1	0.6%
181	1	0.6%
180	1	0.6%

Date Time
Date

Constant

Distinct	1
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	2.8 KiB

Minimum	2025-07-03 00:00:00
Maximum	2025-07-03 00:00:00
Invalid dates	0
Invalid dates (%)	0.0%

Histogram with fixed size bins (bins=1)

Stock
Text

Distinct	72
Distinct (%)	40.0%
Missing	0
Missing (%)	0.0%
Memory size	12.1 KiB

Length

Max length	20
Median length	4
Mean length	4.0944444
Min length	2

Characters and Unicode

Total characters	737
Distinct characters	43
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	33 ?
Unique (%)	18.3%

Sample

1st row	NVDA
2nd row	AMZN
3rd row	TSLA
4th row	AMD
5th row	META

Value	Count	Frequency (%)
intu	8	4.4%
docu	7	3.8%
ntes	7	3.8%
klac	6	3.3%
mrvl	6	3.3%
asml	6	3.3%
adp	6	3.3%
aapl	5	2.7%
rost	5	2.7%
idxx	5	2.7%
Other values (64)	122	66.7%

Most occurring characters

Value	Count	Frequency (%)
A	66	9.0%
S	55	7.5%
C	50	6.8%
T	47	6.4%
L	46	6.2%
N	46	6.2%
M	45	6.1%
D	42	5.7%
I	37	5.0%
O	35	4.7%
Other values (33)	268	36.4%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	687	93.2%
Lowercase Letter	47	6.4%
Space Separator	3	0.4%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
A	66	9.6%
S	55	8.0%
C	50	7.3%
T	47	6.8%
L	46	6.7%
N	46	6.7%
M	45	6.6%
D	42	6.1%
I	37	5.4%
O	35	5.1%
Other values (16)	218	31.7%

Lowercase Letter

Value	Count	Frequency (%)
e	6	12.8%
o	5	10.6%
a	5	10.6%
c	4	8.5%
r	4	8.5%
h	3	6.4%
i	3	6.4%
n	3	6.4%
t	3	6.4%
g	2	4.3%
Other values (6)	9	19.1%

Space Separator

Value	Count	Frequency (%)
	3	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	734	99.6%
Common	3	0.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
A	66	9.0%
S	55	7.5%
C	50	6.8%
T	47	6.4%
L	46	6.3%
N	46	6.3%
M	45	6.1%
D	42	5.7%
I	37	5.0%
O	35	4.8%
Other values (32)	265	36.1%

Common

Value	Count	Frequency (%)
	3	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	737	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
A	66	9.0%
S	55	7.5%
C	50	6.8%
T	47	6.4%
L	46	6.2%
N	46	6.2%
M	45	6.1%
D	42	5.7%
I	37	5.0%
O	35	4.7%
Other values (33)	268	36.4%

Curr_Price
Real number (ℝ)

High correlation Zeros

Distinct	66
Distinct (%)	36.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	269.8969

Minimum	0
Maximum	1297.1801
Zeros	5
Zeros (%)	2.8%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0
5-th percentile	8.9527498
Q1	76.389999
median	133.615
Q3	315.185
95-th percentile	924.65997
Maximum	1297.1801
Range	1297.1801
Interquartile range (IQR)	238.795

Descriptive statistics

Standard deviation	295.56812
Coefficient of variation (CV)	1.0951149
Kurtosis	1.5973332
Mean	269.8969
Median Absolute Deviation (MAD)	83.735001
Skewness	1.5427188
Sum	48581.443
Variance	87360.514
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
132.8600006	12	6.7%
780.7999878	8	4.4%
78.97499847	7	3.9%
794.9799805	6	3.3%
308.9599915	6	3.3%
75.16000366	6	3.3%
924.6599731	6	3.3%
0	5	2.8%
131.6499939	5	2.8%
213.5899963	5	2.8%
Other values (56)	114	63.3%

Value	Count	Frequency (%)
0	5	2.8%
2.164999962	3	1.7%
3.494999886	1	0.6%
9.239999771	1	0.6%
11.19999981	1	0.6%
12.89000034	2	1.1%
13.07999992	1	0.6%
16.14999962	2	1.1%
18.57999992	1	0.6%
22.46999931	2	1.1%

Value	Count	Frequency (%)
1297.180054	3	1.7%
987.4000244	1	0.6%
924.6599731	6	3.3%
794.9799805	6	3.3%
780.7999878	8	4.4%
722	1	0.6%
718.5999756	4	2.2%
561.1049805	1	0.6%
547	5	2.8%
514.1099854	2	1.1%

Buy
Categorical

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	126
1	54

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
0	126	70.0%
1	54	30.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	126	70.0%
1	54	30.0%

Most occurring characters

Value	Count	Frequency (%)
0	126	70.0%
1	54	30.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	126	70.0%
1	54	30.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	126	70.0%
1	54	30.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	126	70.0%
1	54	30.0%

Sell
Categorical

High correlation

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	136
1	44

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	136	75.6%
1	44	24.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	136	75.6%
1	44	24.4%

Most occurring characters

Value	Count	Frequency (%)
0	136	75.6%
1	44	24.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	136	75.6%
1	44	24.4%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	136	75.6%
1	44	24.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	136	75.6%
1	44	24.4%

Hold
Categorical

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	121
1	59

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	1
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	121	67.2%
1	59	32.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	121	67.2%
1	59	32.8%

Most occurring characters

Value	Count	Frequency (%)
0	121	67.2%
1	59	32.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	121	67.2%
1	59	32.8%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	121	67.2%
1	59	32.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	121	67.2%
1	59	32.8%

Day1_Pred
Real number (ℝ)

High correlation

Distinct	125
Distinct (%)	69.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	277.17194

Minimum	2.05
Maximum	1345
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	2.05
5-th percentile	16.8
Q1	81.125
median	141.425
Q3	325.5
95-th percentile	828.825
Maximum	1345
Range	1342.95
Interquartile range (IQR)	244.375

Descriptive statistics

Standard deviation	295.7671
Coefficient of variation (CV)	1.0670889
Kurtosis	2.227002
Mean	277.17194
Median Absolute Deviation (MAD)	85.2
Skewness	1.6571459
Sum	49890.95
Variance	87478.179
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
320	4	2.2%
215	4	2.2%
36.5	4	2.2%
313	4	2.2%
99.5	3	1.7%
80	3	1.7%
140	3	1.7%
725	3	1.7%
230	3	1.7%
74.5	3	1.7%
Other values (115)	146	81.1%

Value	Count	Frequency (%)
2.05	1	0.6%
2.5	2	1.1%
4	1	0.6%
9.5	1	0.6%
12	2	1.1%
13	2	1.1%
17	2	1.1%
19.3	1	0.6%
22.5	2	1.1%
24	1	0.6%

Value	Count	Frequency (%)
1345	2	1.1%
1305	1	0.6%
1234.56	1	0.6%
1015	1	0.6%
960	1	0.6%
950	1	0.6%
930	2	1.1%
823.5	2	1.1%
814.64	2	1.1%
814.23	2	1.1%

closest_day1_price
Real number (ℝ)

High correlation Missing

Distinct	87
Distinct (%)	49.7%
Missing	5
Missing (%)	2.8%
Infinite	0
Infinite (%)	0.0%
Mean	277.19179

Minimum	2.0599999
Maximum	1291.3766
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	2.0599999
5-th percentile	15.298499
Q1	78.835499
median	137.47
Q3	318
95-th percentile	905.90503
Maximum	1291.3766
Range	1289.3166
Interquartile range (IQR)	239.1645

Descriptive statistics

Standard deviation	295.59294
Coefficient of variation (CV)	1.0663842
Kurtosis	1.4766122
Mean	277.19179
Median Absolute Deviation (MAD)	81.970001
Skewness	1.5168568
Sum	48508.563
Variance	87375.184
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
787.5300293	6	3.3%
74.68000031	6	3.3%
79.16999817	6	3.3%
134.1549988	5	2.8%
549.4899902	5	2.8%
295.9299927	4	2.2%
159.1600037	4	2.2%
98.85900116	4	2.2%
215.1900024	4	2.2%
922.7999878	4	2.2%
Other values (77)	127	70.6%
(Missing)	5	2.8%

Value	Count	Frequency (%)
2.059999943	1	0.6%
2.119999886	2	1.1%
3.470000029	1	0.6%
9.31499958	1	0.6%
11.28499985	1	0.6%
12.46000004	1	0.6%
12.88000011	2	1.1%
16.33499908	2	1.1%
19.26499939	1	0.6%
22.37999916	2	1.1%

Value	Count	Frequency (%)
1291.376587	3	1.7%
992.8931274	1	0.6%
922.7999878	4	2.2%
905.9050293	2	1.1%
792.9899902	4	2.2%
787.5300293	6	3.3%
783.8300171	2	1.1%
780.5700073	1	0.6%
778.8400269	1	0.6%
726.5050049	1	0.6%

actual_day1_close
Real number (ℝ)

High correlation Missing

Distinct	66
Distinct (%)	37.7%
Missing	5
Missing (%)	2.8%
Infinite	0
Infinite (%)	0.0%
Mean	275.49703

Minimum	2.105
Maximum	1289.0699
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	2.105
5-th percentile	15.0705
Q1	77.650002
median	136.97
Q3	317.955
95-th percentile	912.90002
Maximum	1289.0699
Range	1286.9649
Interquartile range (IQR)	240.305

Descriptive statistics

Standard deviation	294.70917
Coefficient of variation (CV)	1.0697363
Kurtosis	1.4980579
Mean	275.49703
Median Absolute Deviation (MAD)	81.510002
Skewness	1.5216764
Sum	48211.98
Variance	86853.492
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
783.5499878	8	4.4%
77.65000153	7	3.9%
133.0399933	7	3.9%
308.4899902	6	3.3%
784.9899902	6	3.3%
912.9000244	6	3.3%
71.55999756	6	3.3%
130.7700043	5	2.8%
545.0999756	5	2.8%
209.9400024	5	2.8%
Other values (56)	114	63.3%

Value	Count	Frequency (%)
2.105000019	3	1.7%
3.410000086	1	0.6%
9.18999958	1	0.6%
11.02000046	1	0.6%
12.52000046	2	1.1%
12.75	1	0.6%
16.06500053	2	1.1%
19.25499916	1	0.6%
21.99500084	2	1.1%
23.62000084	1	0.6%

Value	Count	Frequency (%)
1289.069946	3	1.7%
992.210022	1	0.6%
912.9000244	6	3.3%
784.9899902	6	3.3%
783.5499878	8	4.4%
736.4000244	1	0.6%
718.6400146	4	2.2%
555.2399902	1	0.6%
545.0999756	5	2.8%
505.480011	2	1.1%

day1_min_error
Real number (ℝ)

High correlation Missing Zeros

Distinct	128
Distinct (%)	73.1%
Missing	5
Missing (%)	2.8%
Infinite	0
Infinite (%)	0.0%
Mean	3.8623968

Minimum	0
Maximum	68.417134
Zeros	11
Zeros (%)	6.1%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0.58537007
median	1.908839
Q3	3.6805377
95-th percentile	12.773671
Maximum	68.417134
Range	68.417134
Interquartile range (IQR)	3.0951676

Descriptive statistics

Standard deviation	9.1117451
Coefficient of variation (CV)	2.3590909
Kurtosis	32.714533
Mean	3.8623968
Median Absolute Deviation (MAD)	1.4338645
Skewness	5.4646341
Sum	675.91944
Variance	83.023899
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	11	6.1%
0.7321566825	4	2.2%
0.08829519924	4	2.2%
1.024076726	3	1.7%
0.6483970431	3	1.7%
4.611153252	3	1.7%
29.1785224	2	1.1%
2.577824792	2	1.1%
0.7802354034	2	1.1%
0.541809321	2	1.1%
Other values (118)	139	77.2%
(Missing)	5	2.8%

Value	Count	Frequency (%)
0	11	6.1%
2.211075414 × 10^-6	1	0.6%
3.730755136 × 10^-6	1	0.6%
0.001247739466	1	0.6%
0.001273169374	1	0.6%
0.00138067639	1	0.6%
0.003777127375	1	0.6%
0.005552348511	1	0.6%
0.005840289011	2	1.1%
0.008966867177	1	0.6%

Value	Count	Frequency (%)
68.41713426	1	0.6%
62.18588164	1	0.6%
61.84257855	1	0.6%
29.1785224	2	1.1%
26.70515437	1	0.6%
17.92453467	2	1.1%
15.27377427	1	0.6%
11.70219746	1	0.6%
8.133682939	2	1.1%
7.431348209	1	0.6%

day1_act_error
Real number (ℝ)

High correlation Missing

Distinct	136
Distinct (%)	77.7%
Missing	5
Missing (%)	2.8%
Infinite	0
Infinite (%)	0.0%
Mean	5.0784807

Minimum	0.072125705
Maximum	68.606981
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0.072125705
5-th percentile	0.34576574
Q1	1.5784387
median	3.0263985
Q3	5.0503577
95-th percentile	14.211567
Maximum	68.606981
Range	68.534855
Interquartile range (IQR)	3.4719189

Descriptive statistics

Standard deviation	9.024216
Coefficient of variation (CV)	1.7769519
Kurtosis	31.733308
Mean	5.0784807
Median Absolute Deviation (MAD)	1.5644354
Skewness	5.3304903
Sum	888.73412
Variance	81.436475
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2.410211251	4	2.2%
1.461963081	4	2.2%
1.37544417	3	1.7%
2.990974646	3	1.7%
6.721590563	3	1.7%
4.338791225	2	1.1%
3.967840303	2	1.1%
2.917488991	2	1.1%
3.915514349	2	1.1%
3.359896978	2	1.1%
Other values (126)	148	82.2%
(Missing)	5	2.8%

Value	Count	Frequency (%)
0.07212570535	1	0.6%
0.0768686386	1	0.6%
0.1225471519	1	0.6%
0.1435409934	1	0.6%
0.1710762644	1	0.6%
0.2337098997	1	0.6%
0.2375296912	1	0.6%
0.2450999137	1	0.6%
0.3457657374	2	1.1%
0.3713850856	1	0.6%

Value	Count	Frequency (%)
68.60698056	1	0.6%
62.47562813	1	0.6%
62.13495555	1	0.6%
29.2831747	2	1.1%
27.1927836	1	0.6%
18.76484453	2	1.1%
17.30204983	1	0.6%
12.8870743	1	0.6%
9.052159981	1	0.6%
8.999444747	1	0.6%

made_profit_could_day_1
Categorical

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

1	95
0	85

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
1	95	52.8%
0	85	47.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1	95	52.8%
0	85	47.2%

Most occurring characters

Value	Count	Frequency (%)
1	95	52.8%
0	85	47.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	95	52.8%
0	85	47.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	95	52.8%
0	85	47.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	95	52.8%
0	85	47.2%

made_profit_act_day_1
Categorical

High correlation

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	109
1	71

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	109	60.6%
1	71	39.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	109	60.6%
1	71	39.4%

Most occurring characters

Value	Count	Frequency (%)
0	109	60.6%
1	71	39.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	109	60.6%
1	71	39.4%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	109	60.6%
1	71	39.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	109	60.6%
1	71	39.4%

Day3_Pred
Real number (ℝ)

High correlation

Distinct	143
Distinct (%)	79.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	282.07156

Minimum	2
Maximum	1370
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	2
5-th percentile	17.8
Q1	82
median	146.855
Q3	333.8025
95-th percentile	852.825
Maximum	1370
Range	1368
Interquartile range (IQR)	251.8025

Descriptive statistics

Standard deviation	300.75803
Coefficient of variation (CV)	1.0662473
Kurtosis	2.2224381
Mean	282.07156
Median Absolute Deviation (MAD)	85.65
Skewness	1.6568991
Sum	50772.88
Variance	90455.391
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
37.2	4	2.2%
80	3	1.7%
235	3	1.7%
145	3	1.7%
325	3	1.7%
220	3	1.7%
82	3	1.7%
165	2	1.1%
2.8	2	1.1%
22.8	2	1.1%
Other values (133)	152	84.4%

Value	Count	Frequency (%)
2	1	0.6%
2.8	2	1.1%
4.5	1	0.6%
9.8	1	0.6%
11	1	0.6%
13	1	0.6%
13.5	1	0.6%
14	1	0.6%
18	2	1.1%
20.1	1	0.6%

Value	Count	Frequency (%)
1370	2	1.1%
1310	1	0.6%
1267.89	1	0.6%
1030	1	0.6%
980	1	0.6%
970	1	0.6%
935	2	1.1%
848.5	1	0.6%
845.5	1	0.6%
844.19	1	0.6%

closest_day3_price
Real number (ℝ)

High correlation Missing

Distinct	85
Distinct (%)	48.6%
Missing	5
Missing (%)	2.8%
Infinite	0
Infinite (%)	0.0%
Mean	277.87582

Minimum	2.26
Maximum	1288.25
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	2.26
5-th percentile	15.39891
Q1	79.489998
median	140.4791
Q3	321.13995
95-th percentile	915.51001
Maximum	1288.25
Range	1285.99
Interquartile range (IQR)	241.64996

Descriptive statistics

Standard deviation	296.08953
Coefficient of variation (CV)	1.0655462
Kurtosis	1.4491427
Mean	277.87582
Median Absolute Deviation (MAD)	83.440903
Skewness	1.5136796
Sum	48628.268
Variance	87669.008
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
131.0500031	7	3.9%
73.38500214	6	3.3%
79.77999878	6	3.3%
308.6124878	6	3.3%
782.625	6	3.3%
543.3300171	5	2.8%
299.3599854	4	2.2%
931.664917	4	2.2%
61.20999908	4	2.2%
164.2850037	4	2.2%
Other values (75)	123	68.3%
(Missing)	5	2.8%

Value	Count	Frequency (%)
2.25999999	1	0.6%
2.380000114	2	1.1%
3.585099936	1	0.6%
9.364999771	1	0.6%
11.77499962	1	0.6%
12.84500027	2	1.1%
13.07470036	1	0.6%
16.39500046	2	1.1%
20.10000038	1	0.6%
23.13500023	2	1.1%

Value	Count	Frequency (%)
1288.25	3	1.7%
988.0900269	1	0.6%
931.664917	4	2.2%
915.5100098	2	1.1%
800.7999878	3	1.7%
794.460022	1	0.6%
793.2050171	2	1.1%
782.625	6	3.3%
780.6528931	1	0.6%
767.0100098	1	0.6%

actual_day3_close
Real number (ℝ)

High correlation Missing

Distinct	66
Distinct (%)	37.7%
Missing	5
Missing (%)	2.8%
Infinite	0
Infinite (%)	0.0%
Mean	276.61802

Minimum	2.2850001
Maximum	1288.25
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	2.2850001
5-th percentile	14.929
Q1	79.550003
median	138.45
Q3	319.72
95-th percentile	923.29999
Maximum	1288.25
Range	1285.965
Interquartile range (IQR)	240.17

Descriptive statistics

Standard deviation	295.23477
Coefficient of variation (CV)	1.0673013
Kurtosis	1.478732
Mean	276.61802
Median Absolute Deviation (MAD)	82.499996
Skewness	1.5186439
Sum	48408.153
Variance	87163.571
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
769.6900024	8	4.4%
79.55000305	7	3.9%
130.1999969	7	3.9%
308.4100037	6	3.3%
799.4500122	6	3.3%
923.2999878	6	3.3%
72.30000305	6	3.3%
131.2100067	5	2.8%
540.5	5	2.8%
211.1100006	5	2.8%
Other values (56)	114	63.3%

Value	Count	Frequency (%)
2.285000086	3	1.7%
3.494999886	1	0.6%
9.125	1	0.6%
11.52999973	1	0.6%
12.61499977	2	1.1%
13.19999981	1	0.6%
15.67000008	2	1.1%
20.22500038	1	0.6%
23.45999908	2	1.1%
24.28000069	1	0.6%

Value	Count	Frequency (%)
1288.25	3	1.7%
982.0800171	1	0.6%
923.2999878	6	3.3%
799.4500122	6	3.3%
769.6900024	8	4.4%
732.7999878	4	2.2%
722.25	1	0.6%
560.3900146	1	0.6%
540.5	5	2.8%
513.5200195	2	1.1%

day3_min_error
Real number (ℝ)

High correlation Missing Zeros

Distinct	148
Distinct (%)	84.6%
Missing	5
Missing (%)	2.8%
Infinite	0
Infinite (%)	0.0%
Mean	5.8992404

Minimum	0
Maximum	67.245538
Zeros	2
Zeros (%)	1.1%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0.0026793677
Q1	1.8286263
median	3.8993411
Q3	6.7605652
95-th percentile	16.401753
Maximum	67.245538
Range	67.245538
Interquartile range (IQR)	4.9319389

Descriptive statistics

Standard deviation	9.1307012
Coefficient of variation (CV)	1.5477757
Kurtosis	26.752693
Mean	5.8992404
Median Absolute Deviation (MAD)	2.4513177
Skewness	4.7386825
Sum	1032.3671
Variance	83.369704
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
3.28140362	3	1.7%
6.34581797	2	1.1%
4.089223267	2	1.1%
4.817128991	2	1.1%
2.782653866	2	1.1%
0.1385339846	2	1.1%
6.756109248	2	1.1%
0.008386766569	2	1.1%
4.867285398	2	1.1%
2.406552419	2	1.1%
Other values (138)	154	85.6%
(Missing)	5	2.8%

Value	Count	Frequency (%)
0	2	1.1%
1.248418021 × 10^-6	1	0.6%
1.897859293 × 10^-6	1	0.6%
3.045666575 × 10^-6	1	0.6%
3.853229713 × 10^-6	1	0.6%
0.001651574793	1	0.6%
0.002383334331	1	0.6%
0.00255778867	1	0.6%
0.002731472934	1	0.6%
0.004094406068	1	0.6%

Value	Count	Frequency (%)
67.24553828	1	0.6%
62.29314848	1	0.6%
61.53947021	1	0.6%
29.12929345	1	0.6%
28.08290572	1	0.6%
26.09993252	1	0.6%
25.51951357	1	0.6%
17.64705317	2	1.1%
15.8680528	1	0.6%
15.57157357	1	0.6%

day3_act_error
Real number (ℝ)

High correlation Missing

Distinct	149
Distinct (%)	85.1%
Missing	5
Missing (%)	2.8%
Infinite	0
Infinite (%)	0.0%
Mean	6.9560237

Minimum	0.081876065
Maximum	67.359586
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0.081876065
5-th percentile	0.61883869
Q1	2.6335528
median	4.7862152
Q3	7.8276628
95-th percentile	18.428152
Maximum	67.359586
Range	67.27771
Interquartile range (IQR)	5.19411

Descriptive statistics

Standard deviation	9.2189688
Coefficient of variation (CV)	1.3253217
Kurtosis	24.181575
Mean	6.9560237
Median Absolute Deviation (MAD)	2.6110451
Skewness	4.4590356
Sum	1217.3041
Variance	84.989386
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
4.46503612	3	1.7%
2.81329544	2	1.1%
5.428298441	2	1.1%
5.632219495	2	1.1%
6.34581797	2	1.1%
3.079820056	2	1.1%
1.362183205	2	1.1%
8.550195189	2	1.1%
8.38933326	2	1.1%
14.86917621	2	1.1%
Other values (139)	154	85.6%
(Missing)	5	2.8%

Value	Count	Frequency (%)
0.08187606481	1	0.6%
0.09724110253	1	0.6%
0.3115264798	1	0.6%
0.3605806043	1	0.6%
0.3820944104	2	1.1%
0.439978678	1	0.6%
0.5257925286	1	0.6%
0.618048846	1	0.6%
0.6191772008	1	0.6%
0.6472380274	1	0.6%

Value	Count	Frequency (%)
67.35958643	1	0.6%
62.61128511	1	0.6%
61.8639657	1	0.6%
29.68290807	1	0.6%
28.75536902	1	0.6%
28.64469432	1	0.6%
26.39280919	1	0.6%
22.53828861	2	1.1%
16.66666546	1	0.6%
15.74576772	1	0.6%

made_profit_act_day_3
Categorical

High correlation

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	111
1	69

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
0	111	61.7%
1	69	38.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	111	61.7%
1	69	38.3%

Most occurring characters

Value	Count	Frequency (%)
0	111	61.7%
1	69	38.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	111	61.7%
1	69	38.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	111	61.7%
1	69	38.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	111	61.7%
1	69	38.3%

made_profit_could_day_3
Categorical

High correlation

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	98
1	82

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
0	98	54.4%
1	82	45.6%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	98	54.4%
1	82	45.6%

Most occurring characters

Value	Count	Frequency (%)
0	98	54.4%
1	82	45.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	98	54.4%
1	82	45.6%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	98	54.4%
1	82	45.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	98	54.4%
1	82	45.6%

Day7_Pred
Real number (ℝ)

High correlation

Distinct	143
Distinct (%)	79.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	288.22933

Minimum	1.95
Maximum	1400
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	1.95
5-th percentile	18.8
Q1	85
median	154.5
Q3	344.865
95-th percentile	886.4105
Maximum	1400
Range	1398.05
Interquartile range (IQR)	259.865

Descriptive statistics

Standard deviation	307.37862
Coefficient of variation (CV)	1.0664377
Kurtosis	2.2356837
Mean	288.22933
Median Absolute Deviation (MAD)	85.5
Skewness	1.6597508
Sum	51881.28
Variance	94481.617
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
320	4	2.2%
85	3	1.7%
240	3	1.7%
330	3	1.7%
150	3	1.7%
225	3	1.7%
38	3	1.7%
125	3	1.7%
128	2	1.1%
23.2	2	1.1%
Other values (133)	151	83.9%

Value	Count	Frequency (%)
1.95	1	0.6%
3	1	0.6%
3.2	1	0.6%
5	1	0.6%
10	1	0.6%
10.2	1	0.6%
14	1	0.6%
15	2	1.1%
19	2	1.1%
21	1	0.6%

Value	Count	Frequency (%)
1400	2	1.1%
1321.23	1	0.6%
1320	1	0.6%
1050	1	0.6%
1000	2	1.1%
940	2	1.1%
883.59	1	0.6%
883.49	1	0.6%
875.5	2	1.1%
865.5	1	0.6%

closest_day7_price
Real number (ℝ)

High correlation

Distinct	81
Distinct (%)	45.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	285.77946

Minimum	2.3002999
Maximum	1255.1899
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	2.3002999
5-th percentile	15.276755
Q1	75.839996
median	144.1528
Q3	322.95541
95-th percentile	922.09998
Maximum	1255.1899
Range	1252.8896
Interquartile range (IQR)	247.11542

Descriptive statistics

Standard deviation	299.08087
Coefficient of variation (CV)	1.0465443
Kurtosis	0.89427888
Mean	285.77946
Median Absolute Deviation (MAD)	85.352802
Skewness	1.3799284
Sum	51440.303
Variance	89449.365
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
749.3939819	7	3.9%
128.8600006	7	3.9%
75.83999634	7	3.9%
73.15000153	6	3.3%
796.6400146	6	3.3%
305.0700073	6	3.3%
538.9199829	5	2.8%
167.5	4	2.2%
224.7400055	4	2.2%
77.01999664	4	2.2%
Other values (71)	124	68.9%

Value	Count	Frequency (%)
2.300299883	1	0.6%
2.345000029	2	1.1%
4.040100098	1	0.6%
9.385000229	1	0.6%
11.62969971	1	0.6%
12.73999977	2	1.1%
13.16499996	1	0.6%
15.38790035	2	1.1%
21.14119911	1	0.6%
23.20479965	2	1.1%

Value	Count	Frequency (%)
1255.189941	3	1.7%
977.2000122	1	0.6%
929.2550049	4	2.2%
922.0999756	3	1.7%
801.4400024	3	1.7%
796.6400146	6	3.3%
749.3939819	7	3.9%
743.9299927	1	0.6%
724.3150024	4	2.2%
713.8499756	1	0.6%

actual_day7_close
Real number (ℝ)

High correlation

Distinct	108
Distinct (%)	60.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	285.08447

Minimum	2.3106
Maximum	1249.575
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	2.3106
5-th percentile	15.20781
Q1	74.815002
median	143.5078
Q3	322.32999
95-th percentile	928.995
Maximum	1249.575
Range	1247.2644
Interquartile range (IQR)	247.51498

Descriptive statistics

Standard deviation	298.52099
Coefficient of variation (CV)	1.0471317
Kurtosis	0.88952801
Mean	285.08447
Median Absolute Deviation (MAD)	85.037806
Skewness	1.3807798
Sum	51315.204
Variance	89114.781
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
304.1499939	6	3.3%
798.9667969	6	3.3%
928.9949951	5	2.8%
211.5449982	4	2.2%
74.31999969	4	2.2%
128.3099976	4	2.2%
748.670105	4	2.2%
74.81500244	4	2.2%
310.2309875	3	1.7%
716.4799805	3	1.7%
Other values (98)	137	76.1%

Value	Count	Frequency (%)
2.310600042	3	1.7%
3.98239994	1	0.6%
9.164999962	1	0.6%
11.54500008	1	0.6%
12.72500038	1	0.6%
12.72929955	1	0.6%
13.36499977	1	0.6%
15.30480003	2	1.1%
21.69499969	1	0.6%
23.42110062	1	0.6%

Value	Count	Frequency (%)
1249.574951	1	0.6%
1249.070557	2	1.1%
969.4299927	1	0.6%
929.1450195	2	1.1%
928.9949951	5	2.8%
799.2000122	1	0.6%
798.9667969	6	3.3%
798.9500122	2	1.1%
748.75	1	0.6%
748.670105	4	2.2%

day7_min_error
Real number (ℝ)

High correlation

Distinct	156
Distinct (%)	86.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	14.288718

Minimum	0
Maximum	636.6139
Zeros	1
Zeros (%)	0.6%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0.64792322
Q1	4.1699222
median	7.378127
Q3	13.867228
95-th percentile	29.061841
Maximum	636.6139
Range	636.6139
Interquartile range (IQR)	9.6973054

Descriptive statistics

Standard deviation	48.434587
Coefficient of variation (CV)	3.3897085
Kurtosis	154.48937
Mean	14.288718
Median Absolute Deviation (MAD)	4.1081175
Skewness	12.039112
Sum	2571.9692
Variance	2345.9093
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
4.893956245	4	2.2%
7.77085028	3	1.7%
6.790065913	2	1.1%
0.02068387649	2	1.1%
10.75949902	2	1.1%
5.898211017	2	1.1%
1.156302098	2	1.1%
6.868977654	2	1.1%
5.263155699	2	1.1%
7.613087338	2	1.1%
Other values (146)	157	87.2%

Value	Count	Frequency (%)
0	1	0.6%
0.004953946145	1	0.6%
0.006517761517	1	0.6%
0.01010466783	1	0.6%
0.01176860393	1	0.6%
0.02068387649	2	1.1%
0.1611016955	1	0.6%
0.2686300724	1	0.6%
0.6678860134	1	0.6%
1.156302098	2	1.1%

Value	Count	Frequency (%)
636.6138986	1	0.6%
93.64832307	1	0.6%
84.29654578	1	0.6%
65.2507625	1	0.6%
62.05509063	1	0.6%
60.69298237	1	0.6%
43.28489697	1	0.6%
36.46055271	1	0.6%
30.61465271	1	0.6%
28.98011452	1	0.6%

day7_act_error
Real number (ℝ)

High correlation

Distinct	166
Distinct (%)	92.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	14.864559

Minimum	0.049428248
Maximum	643.07795
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0.049428248
5-th percentile	1.3084229
Q1	4.4792625
median	7.7315999
Q3	14.063129
95-th percentile	30.008862
Maximum	643.07795
Range	643.02852
Interquartile range (IQR)	9.5838661

Descriptive statistics

Standard deviation	48.855203
Coefficient of variation (CV)	3.2866905
Kurtosis	154.9701
Mean	14.864559
Median Absolute Deviation (MAD)	4.3291188
Skewness	12.064081
Sum	2675.6206
Variance	2386.8309
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5.211246563	4	2.2%
7.643206396	2	1.1%
6.360349783	2	1.1%
2.584862109	2	1.1%
6.372352616	2	1.1%
9.13626546	2	1.1%
7.024220858	2	1.1%
9.090228646	2	1.1%
24.14405911	2	1.1%
15.49372833	2	1.1%
Other values (156)	158	87.8%

Value	Count	Frequency (%)
0.04942824829	1	0.6%
0.1186032318	1	0.6%
0.176161507	1	0.6%
0.3453364375	1	0.6%
0.5590716526	1	0.6%
0.9440231699	1	0.6%
1.044152346	1	0.6%
1.168276237	1	0.6%
1.184614012	1	0.6%
1.314939129	1	0.6%

Value	Count	Frequency (%)
643.0779454	1	0.6%
93.66682067	1	0.6%
84.34227799	1	0.6%
65.470773	1	0.6%
62.33671851	1	0.6%
60.99101944	1	0.6%
42.22143359	1	0.6%
38.492164	1	0.6%
31.57612915	1	0.6%
29.9263742	1	0.6%

made_profit_could_day_7
Categorical

High correlation

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	112
1	68

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
0	112	62.2%
1	68	37.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	112	62.2%
1	68	37.8%

Most occurring characters

Value	Count	Frequency (%)
0	112	62.2%
1	68	37.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	112	62.2%
1	68	37.8%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	112	62.2%
1	68	37.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	112	62.2%
1	68	37.8%

made_profit_act_day_7
Categorical

High correlation

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	118
1	62

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
0	118	65.6%
1	62	34.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	118	65.6%
1	62	34.4%

Most occurring characters

Value	Count	Frequency (%)
0	118	65.6%
1	62	34.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	118	65.6%
1	62	34.4%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	118	65.6%
1	62	34.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	118	65.6%
1	62	34.4%

Query
Text

Distinct	75
Distinct (%)	41.7%
Missing	0
Missing (%)	0.0%
Memory size	47.7 KiB

Length

Max length	160
Median length	125
Mean length	113.97222
Min length	68

Characters and Unicode

Total characters	20515
Distinct characters	83
Distinct categories	12 ?
Distinct scripts	2 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	19 ?
Unique (%)	10.6%

Sample

1st row	1. I bought Nvidia (NVDA) at $960 but it’s now near $1,200. Should I trim my position or continue riding the AI wave?
2nd row	2. With Amazon (AMZN) up after its Prime Day preview, is it smart to buy now or wait for a post‑event dip?
3rd row	3. Tesla (TSLA) just announced another price cut in Europe. Does that make the stock a buy, sell, or hold for the next 12 months?
4th row	4. After the recent pullback in Advanced Micro Devices (AMD), is this an attractive entry point or a value trap?
5th row	5. Is Meta Platforms (META) still a good buy given its metaverse spending, or should I rotate into Alphabet (GOOGL)?

Value	Count	Frequency (%)
and	157	4.4%
i	122	3.4%
or	120	3.4%
should	87	2.5%
is	59	1.7%
a	57	1.6%
the	56	1.6%
to	55	1.5%
for	52	1.5%
in	52	1.5%
Other values (575)	2733	77.0%

Most occurring characters

Value	Count	Frequency (%)
	3369	16.4%
o	1317	6.4%
e	1242	6.1%
t	1091	5.3%
a	926	4.5%
i	904	4.4%
r	881	4.3%
n	833	4.1%
s	735	3.6%
d	637	3.1%
Other values (73)	8580	41.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	12365	60.3%
Space Separator	3370	16.4%
Uppercase Letter	2735	13.3%
Other Punctuation	716	3.5%
Decimal Number	451	2.2%
Close Punctuation	275	1.3%
Open Punctuation	275	1.3%
Control	190	0.9%
Dash Punctuation	79	0.4%
Final Punctuation	47	0.2%
Other values (2)	12	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	1317	10.7%
e	1242	10.0%
t	1091	8.8%
a	926	7.5%
i	904	7.3%
r	881	7.1%
n	833	6.7%
s	735	5.9%
d	637	5.2%
l	588	4.8%
Other values (16)	3211	26.0%

Uppercase Letter

Value	Count	Frequency (%)
I	320	11.7%
A	265	9.7%
S	257	9.4%
M	176	6.4%
L	166	6.1%
N	165	6.0%
C	160	5.9%
T	144	5.3%
D	135	4.9%
P	125	4.6%
Other values (16)	822	30.1%

Decimal Number

Value	Count	Frequency (%)
2	74	16.4%
4	58	12.9%
1	57	12.6%
3	49	10.9%
6	49	10.9%
0	39	8.6%
5	34	7.5%
8	33	7.3%
7	32	7.1%
9	26	5.8%

Other Punctuation

Value	Count	Frequency (%)
.	268	37.4%
,	254	35.5%
?	183	25.6%
%	6	0.8%
'	3	0.4%
;	2	0.3%

Dash Punctuation

Value	Count	Frequency (%)
-	55	69.6%
—	13	16.5%
‑	8	10.1%
–	3	3.8%

Space Separator

Value	Count	Frequency (%)
	3369	> 99.9%
	1	< 0.1%

Control

Value	Count	Frequency (%)
	95	50.0%
	95	50.0%

Math Symbol

Value	Count	Frequency (%)
+	4	57.1%
~	3	42.9%

Currency Symbol

Value	Count	Frequency (%)
₹	3	60.0%
$	2	40.0%

Close Punctuation

Value	Count	Frequency (%)
)	275	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	275	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	47	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	15100	73.6%
Common	5415	26.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	1317	8.7%
e	1242	8.2%
t	1091	7.2%
a	926	6.1%
i	904	6.0%
r	881	5.8%
n	833	5.5%
s	735	4.9%
d	637	4.2%
l	588	3.9%
Other values (42)	5946	39.4%

Common

Value	Count	Frequency (%)
	3369	62.2%
)	275	5.1%
(	275	5.1%
.	268	4.9%
,	254	4.7%
?	183	3.4%
	95	1.8%
	95	1.8%
2	74	1.4%
4	58	1.1%
Other values (21)	469	8.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	20440	99.6%
Punctuation	72	0.4%
Currency Symbols	3	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	3369	16.5%
o	1317	6.4%
e	1242	6.1%
t	1091	5.3%
a	926	4.5%
i	904	4.4%
r	881	4.3%
n	833	4.1%
s	735	3.6%
d	637	3.1%
Other values (67)	8505	41.6%

Punctuation

Value	Count	Frequency (%)
’	47	65.3%
—	13	18.1%
‑	8	11.1%
–	3	4.2%
	1	1.4%

Currency Symbols

Value	Count	Frequency (%)
₹	3	100.0%

Advice
Text

Distinct	75
Distinct (%)	41.7%
Missing	0
Missing (%)	0.0%
Memory size	68.1 KiB

Length

Max length	761
Median length	349
Mean length	315.04444
Min length	4

Characters and Unicode

Total characters	56708
Distinct characters	78
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	19 ?
Unique (%)	10.6%

Sample

1st row	Continue riding the AI wave, but consider trimming your position by 10-20% to lock in some profits.
2nd row	Buy now, as the stock is likely to continue its upward trend after the Prime Day preview. However, keep an eye on the market news and adjust your strategy accordingly.
3rd row	Hold
4th row	Buy, as the recent pullback in AMD presents an attractive entry point due to its strong revenue growth in the data center segment and Cathie Wood's confidence in its growth prospects.
5th row	{'stock': 'META', 'reason': 'Meta Platforms, Inc. is still a somewhat bullish stock due to its growing metaverse spending, which is seen as a promising area for future growth.', 'rotation': 'No, do not rotate into Alphabet (GOOGL) as it is a somewhat bearish stock due to the decline in search queries and the increasing competition from AI chatbots.'}

Value	Count	Frequency (%)
and	440	5.0%
the	380	4.3%
to	355	4.0%
a	322	3.6%
portfolio	140	1.6%
is	132	1.5%
your	128	1.4%
on	120	1.4%
growth	116	1.3%
for	113	1.3%
Other values (716)	6633	74.7%

Most occurring characters

Value	Count	Frequency (%)
	8699	15.3%
e	4654	8.2%
t	3922	6.9%
o	3865	6.8%
n	3254	5.7%
i	3202	5.6%
a	3179	5.6%
s	2877	5.1%
r	2708	4.8%
d	1724	3.0%
Other values (68)	18624	32.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	41121	72.5%
Space Separator	8699	15.3%
Uppercase Letter	3358	5.9%
Other Punctuation	2544	4.5%
Decimal Number	310	0.5%
Close Punctuation	238	0.4%
Open Punctuation	238	0.4%
Dash Punctuation	135	0.2%
Connector Punctuation	61	0.1%
Math Symbol	4	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	4654	11.3%
t	3922	9.5%
o	3865	9.4%
n	3254	7.9%
i	3202	7.8%
a	3179	7.7%
s	2877	7.0%
r	2708	6.6%
d	1724	4.2%
l	1694	4.1%
Other values (16)	10042	24.4%

Uppercase Letter

Value	Count	Frequency (%)
A	290	8.6%
I	272	8.1%
S	272	8.1%
T	238	7.1%
N	224	6.7%
B	219	6.5%
C	215	6.4%
M	183	5.4%
H	177	5.3%
L	170	5.1%
Other values (16)	1098	32.7%

Decimal Number

Value	Count	Frequency (%)
0	93	30.0%
2	65	21.0%
3	57	18.4%
5	30	9.7%
1	27	8.7%
4	21	6.8%
6	7	2.3%
7	6	1.9%
8	4	1.3%

Other Punctuation

Value	Count	Frequency (%)
'	984	38.7%
,	684	26.9%
.	503	19.8%
:	254	10.0%
"	64	2.5%
%	55	2.2%

Close Punctuation

Value	Count	Frequency (%)
)	130	54.6%
}	93	39.1%
]	15	6.3%

Open Punctuation

Value	Count	Frequency (%)
(	130	54.6%
{	93	39.1%
[	15	6.3%

Dash Punctuation

Value	Count	Frequency (%)
-	132	97.8%
–	3	2.2%

Space Separator

Value	Count	Frequency (%)
	8699	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	61	100.0%

Math Symbol

Value	Count	Frequency (%)
+	4	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	44479	78.4%
Common	12229	21.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	4654	10.5%
t	3922	8.8%
o	3865	8.7%
n	3254	7.3%
i	3202	7.2%
a	3179	7.1%
s	2877	6.5%
r	2708	6.1%
d	1724	3.9%
l	1694	3.8%
Other values (42)	13400	30.1%

Common

Value	Count	Frequency (%)
	8699	71.1%
'	984	8.0%
,	684	5.6%
.	503	4.1%
:	254	2.1%
-	132	1.1%
)	130	1.1%
(	130	1.1%
0	93	0.8%
}	93	0.8%
Other values (16)	527	4.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	56705	> 99.9%
Punctuation	3	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	8699	15.3%
e	4654	8.2%
t	3922	6.9%
o	3865	6.8%
n	3254	5.7%
i	3202	5.6%
a	3179	5.6%
s	2877	5.1%
r	2708	4.8%
d	1724	3.0%
Other values (67)	18621	32.8%

Punctuation

Value	Count	Frequency (%)
–	3	100.0%

profit_1
Boolean

High correlation

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	1.6 KiB

False	94
True	86

Value	Count	Frequency (%)
False	94	52.2%
True	86	47.8%

profit_3
Boolean

High correlation

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	1.6 KiB

False	99
True	81

Value	Count	Frequency (%)
False	99	55.0%
True	81	45.0%

profit_7
Boolean

High correlation

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	1.6 KiB

False	106
True	74

Value	Count	Frequency (%)
False	106	58.9%
True	74	41.1%

Category
Categorical

Distinct	13
Distinct (%)	7.2%
Missing	0
Missing (%)	0.0%
Memory size	13.3 KiB

Technology	57
Semiconductors	26
Consumer	25
Healthcare	11
Media Entertainment	11
Other values (8)	50

Length

Max length	19
Median length	14
Mean length	10.744444
Min length	7

Characters and Unicode

Total characters	1934
Distinct characters	31
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.6%

Sample

1st row	Technology
2nd row	Consumer
3rd row	EV Auto
4th row	Technology
5th row	Technology

Common Values

Value	Count	Frequency (%)
Technology	57	31.7%
Semiconductors	26	14.4%
Consumer	25	13.9%
Healthcare	11	6.1%
Media Entertainment	11	6.1%
Real Estate	10	5.6%
Cloud Data	10	5.6%
FinTech	9	5.0%
EV Auto	9	5.0%
Cybersecurity	4	2.2%
Other values (3)	8	4.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
technology	57	25.4%
semiconductors	26	11.6%
consumer	25	11.2%
healthcare	11	4.9%
media	11	4.9%
entertainment	11	4.9%
real	10	4.5%
estate	10	4.5%
cloud	10	4.5%
data	10	4.5%
Other values (8)	43	19.2%

Most occurring characters

Value	Count	Frequency (%)
o	224	11.6%
e	209	10.8%
n	158	8.2%
c	137	7.1%
t	120	6.2%
l	88	4.6%
a	88	4.6%
r	85	4.4%
h	77	4.0%
u	77	4.0%
Other values (21)	671	34.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1648	85.2%
Uppercase Letter	242	12.5%
Space Separator	44	2.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	224	13.6%
e	209	12.7%
n	158	9.6%
c	137	8.3%
t	120	7.3%
l	88	5.3%
a	88	5.3%
r	85	5.2%
h	77	4.7%
u	77	4.7%
Other values (8)	385	23.4%

Uppercase Letter

Value	Count	Frequency (%)
T	66	27.3%
C	42	17.4%
E	30	12.4%
S	26	10.7%
R	14	5.8%
H	11	4.5%
M	11	4.5%
D	10	4.1%
F	10	4.1%
V	9	3.7%
Other values (2)	13	5.4%

Space Separator

Value	Count	Frequency (%)
	44	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1890	97.7%
Common	44	2.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	224	11.9%
e	209	11.1%
n	158	8.4%
c	137	7.2%
t	120	6.3%
l	88	4.7%
a	88	4.7%
r	85	4.5%
h	77	4.1%
u	77	4.1%
Other values (20)	627	33.2%

Common

Value	Count	Frequency (%)
	44	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1934	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
o	224	11.6%
e	209	10.8%
n	158	8.2%
c	137	7.1%
t	120	6.2%
l	88	4.6%
a	88	4.6%
r	85	4.4%
h	77	4.0%
u	77	4.0%
Other values (21)	671	34.7%

missed_opportunity_day1
Categorical

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	140
1	40

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	140	77.8%
1	40	22.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	140	77.8%
1	40	22.2%

Most occurring characters

Value	Count	Frequency (%)
0	140	77.8%
1	40	22.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	140	77.8%
1	40	22.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	140	77.8%
1	40	22.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	140	77.8%
1	40	22.2%

missed_opportunity_day3
Categorical

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	160
1	20

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	160	88.9%
1	20	11.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	160	88.9%
1	20	11.1%

Most occurring characters

Value	Count	Frequency (%)
0	160	88.9%
1	20	11.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	160	88.9%
1	20	11.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	160	88.9%
1	20	11.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	160	88.9%
1	20	11.1%

missed_opportunity_day7
Categorical

High correlation Imbalance

Distinct	2
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Memory size	11.6 KiB

0	170
1	10

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	180
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	170	94.4%
1	10	5.6%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	170	94.4%
1	10	5.6%

Most occurring characters

Value	Count	Frequency (%)
0	170	94.4%
1	10	5.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	180	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	170	94.4%
1	10	5.6%

Most occurring scripts

Value	Count	Frequency (%)
Common	180	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	170	94.4%
1	10	5.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	180	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	170	94.4%
1	10	5.6%

Date
Date

Constant

Distinct	1
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	2.8 KiB

Minimum	2025-07-03 00:00:00
Maximum	2025-07-03 00:00:00
Invalid dates	0
Invalid dates (%)	0.0%

Histogram with fixed size bins (bins=1)

Interactions

Correlations

	Buy	Category	Curr_Price	Day1_Pred	Day3_Pred	Day7_Pred	Hold	Sell	Unnamed: 0	actual_day1_close	actual_day3_close	actual_day7_close	closest_day1_price	closest_day3_price	closest_day7_price	day1_act_error	day1_min_error	day3_act_error	day3_min_error	day7_act_error	day7_min_error	made_profit_act_day_1	made_profit_act_day_3	made_profit_act_day_7	made_profit_could_day_1	made_profit_could_day_3	made_profit_could_day_7	missed_opportunity_day1	missed_opportunity_day3	missed_opportunity_day7	profit_1	profit_3	profit_7
Buy	1.000	0.076	0.000	0.000	0.000	0.000	0.439	0.351	0.127	0.031	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.058	0.000	0.000	0.000	0.000	0.000	0.000	0.061	0.000	0.188	0.090	0.000
Category	0.076	1.000	0.460	0.370	0.378	0.355	0.201	0.000	0.164	0.464	0.465	0.454	0.464	0.456	0.454	0.223	0.214	0.159	0.119	0.000	0.000	0.289	0.087	0.134	0.239	0.182	0.165	0.288	0.358	0.101	0.198	0.000	0.000
Curr_Price	0.000	0.460	1.000	0.911	0.910	0.907	0.000	0.191	-0.044	0.999	0.998	0.882	0.999	0.999	0.884	-0.064	0.098	-0.128	-0.088	-0.153	-0.154	0.210	0.000	0.091	0.169	0.000	0.174	0.295	0.228	0.541	0.075	0.000	0.000
Day1_Pred	0.000	0.370	0.911	1.000	1.000	0.998	0.000	0.235	-0.032	0.992	0.992	0.949	0.995	0.994	0.952	-0.071	0.094	-0.137	-0.095	-0.070	-0.070	0.262	0.033	0.000	0.048	0.131	0.186	0.197	0.330	0.419	0.197	0.000	0.000
Day3_Pred	0.000	0.378	0.910	1.000	1.000	0.999	0.000	0.235	-0.033	0.992	0.991	0.948	0.994	0.993	0.951	-0.070	0.097	-0.134	-0.092	-0.065	-0.065	0.248	0.130	0.000	0.000	0.124	0.209	0.197	0.357	0.381	0.184	0.000	0.000
Day7_Pred	0.000	0.355	0.907	0.998	0.999	1.000	0.000	0.211	-0.037	0.990	0.990	0.947	0.992	0.992	0.950	-0.061	0.104	-0.124	-0.082	-0.054	-0.054	0.225	0.131	0.000	0.053	0.118	0.203	0.168	0.355	0.381	0.155	0.000	0.000
Hold	0.439	0.201	0.000	0.000	0.000	0.000	1.000	0.377	0.085	0.072	0.000	0.000	0.000	0.000	0.000	0.169	0.195	0.138	0.185	0.000	0.000	0.199	0.058	0.000	0.103	0.165	0.000	0.287	0.000	0.000	0.317	0.000	0.051
Sell	0.351	0.000	0.191	0.235	0.235	0.211	0.377	1.000	0.136	0.187	0.211	0.182	0.186	0.186	0.182	0.000	0.000	0.087	0.115	0.000	0.000	0.583	0.130	0.186	0.040	0.000	0.168	0.247	0.064	0.000	0.474	0.000	0.089
Unnamed: 0	0.127	0.164	-0.044	-0.032	-0.033	-0.037	0.085	0.136	1.000	-0.010	-0.010	0.007	-0.014	-0.013	0.003	-0.050	-0.065	-0.005	-0.010	0.074	0.083	0.072	0.000	0.000	0.000	0.050	0.067	0.186	0.068	0.136	0.173	0.000	0.000
actual_day1_close	0.031	0.464	0.999	0.992	0.992	0.990	0.072	0.187	-0.010	1.000	0.998	0.997	0.999	0.998	0.997	-0.077	0.089	-0.135	-0.090	-0.084	-0.084	0.210	0.000	0.106	0.099	0.028	0.137	0.313	0.208	0.521	0.105	0.000	0.000
actual_day3_close	0.000	0.465	0.998	0.992	0.991	0.990	0.000	0.211	-0.010	0.998	1.000	0.999	0.998	1.000	0.999	-0.074	0.090	-0.142	-0.102	-0.092	-0.091	0.248	0.008	0.274	0.135	0.000	0.239	0.285	0.248	0.298	0.132	0.000	0.107
actual_day7_close	0.000	0.454	0.882	0.949	0.948	0.947	0.000	0.182	0.007	0.997	0.999	1.000	0.996	0.998	0.999	-0.067	0.096	-0.138	-0.099	-0.049	-0.048	0.330	0.000	0.225	0.241	0.000	0.168	0.321	0.222	0.288	0.225	0.000	0.000
closest_day1_price	0.000	0.464	0.999	0.995	0.994	0.992	0.000	0.186	-0.014	0.999	0.998	0.996	1.000	0.999	0.997	-0.074	0.091	-0.134	-0.090	-0.083	-0.083	0.178	0.000	0.105	0.133	0.000	0.159	0.293	0.211	0.527	0.000	0.000	0.000
closest_day3_price	0.000	0.456	0.999	0.994	0.993	0.992	0.000	0.186	-0.013	0.998	1.000	0.998	0.999	1.000	0.999	-0.070	0.093	-0.139	-0.098	-0.088	-0.088	0.198	0.000	0.120	0.086	0.000	0.126	0.293	0.238	0.487	0.062	0.000	0.000
closest_day7_price	0.000	0.454	0.884	0.952	0.951	0.950	0.000	0.182	0.003	0.997	0.999	0.999	0.997	0.999	1.000	-0.068	0.096	-0.139	-0.099	-0.051	-0.050	0.330	0.000	0.225	0.241	0.000	0.168	0.321	0.222	0.288	0.225	0.000	0.000
day1_act_error	0.000	0.223	-0.064	-0.071	-0.070	-0.061	0.169	0.000	-0.050	-0.077	-0.074	-0.067	-0.074	-0.070	-0.068	1.000	0.902	0.770	0.764	0.635	0.639	0.124	0.064	0.056	0.240	0.085	0.074	0.000	0.000	0.000	0.000	0.110	0.037
day1_min_error	0.000	0.214	0.098	0.094	0.097	0.104	0.195	0.000	-0.065	0.089	0.090	0.096	0.091	0.093	0.096	0.902	1.000	0.738	0.748	0.644	0.645	0.116	0.140	0.123	0.259	0.164	0.139	0.000	0.000	0.000	0.000	0.114	0.082
day3_act_error	0.000	0.159	-0.128	-0.137	-0.134	-0.124	0.138	0.087	-0.005	-0.135	-0.142	-0.138	-0.134	-0.139	-0.139	0.770	0.738	1.000	0.971	0.874	0.877	0.157	0.146	0.132	0.201	0.118	0.218	0.059	0.000	0.000	0.063	0.000	0.000
day3_min_error	0.000	0.119	-0.088	-0.095	-0.092	-0.082	0.185	0.115	-0.010	-0.090	-0.102	-0.099	-0.090	-0.098	-0.099	0.764	0.748	0.971	1.000	0.875	0.877	0.174	0.184	0.153	0.268	0.168	0.201	0.071	0.000	0.000	0.210	0.142	0.083
day7_act_error	0.000	0.000	-0.153	-0.070	-0.065	-0.054	0.000	0.000	0.074	-0.084	-0.092	-0.049	-0.083	-0.088	-0.051	0.635	0.644	0.874	0.875	1.000	0.996	0.000	0.055	0.028	0.120	0.089	0.051	0.000	0.000	0.000	0.000	0.086	0.069
day7_min_error	0.000	0.000	-0.154	-0.070	-0.065	-0.054	0.000	0.000	0.083	-0.084	-0.091	-0.048	-0.083	-0.088	-0.050	0.639	0.645	0.877	0.877	0.996	1.000	0.000	0.055	0.028	0.120	0.089	0.051	0.000	0.000	0.000	0.000	0.086	0.069
made_profit_act_day_1	0.058	0.289	0.210	0.262	0.248	0.225	0.199	0.583	0.072	0.210	0.248	0.330	0.178	0.198	0.330	0.124	0.116	0.157	0.174	0.000	0.000	1.000	0.179	0.124	0.382	0.220	0.215	0.412	0.000	0.160	0.831	0.032	0.000
made_profit_act_day_3	0.000	0.087	0.000	0.033	0.130	0.131	0.058	0.130	0.000	0.000	0.008	0.000	0.000	0.000	0.000	0.064	0.140	0.146	0.184	0.055	0.055	0.179	1.000	0.592	0.170	0.688	0.620	0.000	0.250	0.037	0.000	0.859	0.463
made_profit_act_day_7	0.000	0.134	0.091	0.000	0.000	0.000	0.000	0.186	0.000	0.106	0.274	0.225	0.105	0.120	0.225	0.056	0.123	0.132	0.153	0.028	0.028	0.124	0.592	1.000	0.000	0.519	0.821	0.054	0.000	0.131	0.000	0.480	0.855
made_profit_could_day_1	0.000	0.239	0.169	0.048	0.000	0.053	0.103	0.040	0.000	0.099	0.135	0.241	0.133	0.086	0.241	0.240	0.259	0.201	0.268	0.120	0.120	0.382	0.170	0.000	1.000	0.287	0.105	0.488	0.073	0.192	0.189	0.000	0.128
made_profit_could_day_3	0.000	0.182	0.000	0.131	0.124	0.118	0.165	0.000	0.050	0.028	0.000	0.000	0.000	0.000	0.000	0.085	0.164	0.118	0.168	0.089	0.089	0.220	0.688	0.519	0.287	1.000	0.607	0.000	0.362	0.123	0.000	0.548	0.374
made_profit_could_day_7	0.000	0.165	0.174	0.186	0.209	0.203	0.000	0.168	0.067	0.137	0.239	0.168	0.159	0.126	0.168	0.074	0.139	0.218	0.201	0.051	0.051	0.215	0.620	0.821	0.105	0.607	1.000	0.000	0.000	0.277	0.054	0.500	0.686
missed_opportunity_day1	0.000	0.288	0.295	0.197	0.197	0.168	0.287	0.247	0.186	0.313	0.285	0.321	0.293	0.293	0.321	0.000	0.000	0.059	0.071	0.000	0.000	0.412	0.000	0.054	0.488	0.000	0.000	1.000	0.000	0.000	0.494	0.000	0.144
missed_opportunity_day3	0.061	0.358	0.228	0.330	0.357	0.355	0.000	0.064	0.068	0.208	0.248	0.222	0.211	0.238	0.222	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.250	0.000	0.073	0.362	0.000	0.000	1.000	0.000	0.000	0.293	0.000
missed_opportunity_day7	0.000	0.101	0.541	0.419	0.381	0.381	0.000	0.000	0.136	0.521	0.298	0.288	0.527	0.487	0.288	0.000	0.000	0.000	0.000	0.000	0.000	0.160	0.037	0.131	0.192	0.123	0.277	0.000	0.000	1.000	0.109	0.000	0.162
profit_1	0.188	0.198	0.075	0.197	0.184	0.155	0.317	0.474	0.173	0.105	0.132	0.225	0.000	0.062	0.225	0.000	0.000	0.063	0.210	0.000	0.000	0.831	0.000	0.000	0.189	0.000	0.054	0.494	0.000	0.109	1.000	0.133	0.057
profit_3	0.090	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.110	0.114	0.000	0.142	0.086	0.086	0.032	0.859	0.480	0.000	0.548	0.500	0.000	0.293	0.000	0.133	1.000	0.592
profit_7	0.000	0.000	0.000	0.000	0.000	0.000	0.051	0.089	0.000	0.000	0.107	0.000	0.000	0.000	0.000	0.037	0.082	0.000	0.083	0.069	0.069	0.000	0.463	0.855	0.128	0.374	0.686	0.144	0.000	0.162	0.057	0.592	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	Unnamed: 0	Date Time	Stock	Curr_Price	Buy	Sell	Hold	Day1_Pred	closest_day1_price	actual_day1_close	day1_min_error	day1_act_error	made_profit_could_day_1	made_profit_act_day_1	Day3_Pred	closest_day3_price	actual_day3_close	day3_min_error	day3_act_error	made_profit_act_day_3	made_profit_could_day_3	Day7_Pred	closest_day7_price	actual_day7_close	day7_min_error	day7_act_error	made_profit_could_day_7	made_profit_act_day_7	Query	Advice	profit_1	profit_3	profit_7	Category	missed_opportunity_day1	missed_opportunity_day3	missed_opportunity_day7	Date
0	0	2025-07-03 00:00	NVDA	159.271103	0	1	0	162.50	159.160004	158.240005	2.098515	2.692110	1	1	165.80	164.285004	162.830002	0.922176	1.823987	0	0	170.20	167.500000	167.359894	1.611940	1.697005	0	0	1. I bought Nvidia (NVDA) at $960 but it’s now near $1,200. Should I trim my position or continue riding the AI wave?\r\n	Continue riding the AI wave, but consider trimming your position by 10-20% to lock in some profits.	True	False	False	Technology	0	0	0	2025-07-03
1	1	2025-07-03 00:00	AMZN	223.289993	1	0	0	230.00	224.220001	223.479996	2.577825	2.917489	1	1	235.00	224.199997	222.470001	4.817129	5.632219	0	1	240.00	224.740005	224.375595	6.790066	6.963505	1	1	2. With Amazon (AMZN) up after its Prime Day preview, is it smart to buy now or wait for a post‑event dip?\r\n	Buy now, as the stock is likely to continue its upward trend after the Prime Day preview. However, keep an eye on the market news and adjust your strategy accordingly.	True	False	True	Consumer	0	1	0	2025-07-03
2	2	2025-07-03 00:00	TSLA	315.184998	0	0	1	310.23	295.929993	293.920013	4.832226	5.549124	0	0	308.56	299.359985	295.820007	3.073228	4.306670	0	0	306.89	306.910004	310.979187	0.006518	1.314939	0	0	3. Tesla (TSLA) just announced another price cut in Europe. Does that make the stock a buy, sell, or hold for the next 12 months?\r\n	Hold	False	False	False	EV Auto	0	0	0	2025-07-03
3	3	2025-07-03 00:00	AMD	137.949997	1	0	0	145.00	137.175003	134.830002	5.704390	7.542830	0	0	150.00	140.479095	138.449997	6.777453	8.342364	1	1	155.00	144.070007	144.047302	7.586584	7.603542	1	1	4. After the recent pullback in Advanced Micro Devices (AMD), is this an attractive entry point or a value trap?\r\n	Buy, as the recent pullback in AMD presents an attractive entry point due to its strong revenue growth in the data center segment and Cathie Wood's confidence in its growth prospects.	False	True	True	Technology	0	0	0	2025-07-03
4	4	2025-07-03 00:00	META	718.599976	0	0	1	732.00	726.505005	718.640015	0.756360	1.859065	1	1	745.00	737.109985	732.799988	1.070399	1.664849	1	1	760.00	724.315002	716.619995	4.926724	6.053418	1	0	5. Is Meta Platforms (META) still a good buy given its metaverse spending, or should I rotate into Alphabet (GOOGL)?\r\n	{'stock': 'META', 'reason': 'Meta Platforms, Inc. is still a somewhat bullish stock due to its growing metaverse spending, which is seen as a promising area for future growth.', 'rotation': 'No, do not rotate into Alphabet (GOOGL) as it is a somewhat bearish stock due to the decline in search queries and the increasing competition from AI chatbots.'}	True	True	False	Technology	0	0	1	2025-07-03
5	5	2025-07-03 00:00	GOOGL	179.490005	0	1	0	182.00	179.029999	176.789993	1.658941	2.947003	1	1	190.00	179.320007	176.695007	5.955829	7.529920	1	1	200.00	177.787796	177.445007	12.493661	12.710976	1	1	5. Is Meta Platforms (META) still a good buy given its metaverse spending, or should I rotate into Alphabet (GOOGL)?\r\n	{'stock': 'META', 'reason': 'Meta Platforms, Inc. is still a somewhat bullish stock due to its growing metaverse spending, which is seen as a promising area for future growth.', 'rotation': 'No, do not rotate into Alphabet (GOOGL) as it is a somewhat bearish stock due to the decline in search queries and the increasing competition from AI chatbots.'}	True	True	True	Technology	0	0	0	2025-07-03
6	6	2025-07-03 00:00	AAPL	213.589996	0	0	1	208.50	208.894608	209.940002	0.188903	0.685911	0	0	206.80	207.330093	211.110001	0.255676	2.041590	0	0	205.10	210.000000	211.789993	2.333333	3.158786	0	0	6. With Apple (AAPL) launching new Vision Pro features, should I increase my allocation or wait for earnings clarity?\r\n	Wait for earnings clarity before making any investment decisions. The decline in searches on Safari may impact Apple's business, but the extent of this impact is unclear. Consider increasing your allocation after earnings clarity is provided.	False	False	False	Technology	0	0	0	2025-07-03
7	7	2025-07-03 00:00	INTU	780.799988	1	0	0	814.64	787.530029	783.549988	3.442405	3.967840	1	1	843.19	782.625000	769.690002	7.738700	9.549299	0	1	883.49	749.393982	748.750000	17.893928	17.995326	0	0	7. Should I add to my position in Intuit (INTU) before the next tax season, or is most of the growth priced in?\r\n	Based on the analysis, it is recommended to add to your position in Intuit (INTU) before the next tax season. The company's strong performance and growth potential make it a good choice to add to your position. However, it's essential to keep an eye on the market and adjust your strategy accordingly.	True	False	False	FinTech	0	1	0	2025-07-03
8	8	2025-07-03 00:00	AVGO	275.179993	1	0	0	285.00	277.329987	274.179993	2.765663	3.946315	1	0	295.00	279.290009	277.820007	5.624974	6.183857	1	1	310.00	274.480011	273.820007	12.940829	13.213057	0	0	8. Broadcom (AVGO) just completed its VMware integration. Buy on synergy optimism or lock in profits?\r\n	Buy on synergy optimism	False	True	False	Technology	1	0	0	2025-07-03
9	9	2025-07-03 00:00	NFLX	1297.180054	1	0	0	1345.00	1291.376587	1289.069946	4.152423	4.338791	0	0	1370.00	1288.250000	1288.250000	6.345818	6.345818	0	0	1400.00	1255.189941	1249.574951	11.536904	12.038097	0	0	10. Netflix (NFLX) introduced ad‑supported tiers; does that warrant a fresh buy, or should I stick with Paramount Streaming (PARA)?\r\n	Based on the analysis, we recommend buying Netflix (NFLX) due to its strong growth prospects and recent introduction of ad-supported tiers. Paramount Streaming (PARA) has a mixed performance, and its current price may not be the best entry point. However, it's essential to monitor PARA's progress and adjust your investment strategy accordingly.	False	False	False	Media Entertainment	0	0	0	2025-07-03

	Unnamed: 0	Date Time	Stock	Curr_Price	Buy	Sell	Hold	Day1_Pred	closest_day1_price	actual_day1_close	day1_min_error	day1_act_error	made_profit_could_day_1	made_profit_act_day_1	Day3_Pred	closest_day3_price	actual_day3_close	day3_min_error	day3_act_error	made_profit_act_day_3	made_profit_could_day_3	Day7_Pred	closest_day7_price	actual_day7_close	day7_min_error	day7_act_error	made_profit_could_day_7	made_profit_act_day_7	Query	Advice	profit_1	profit_3	profit_7	Category	missed_opportunity_day3	missed_opportunity_day7	Date
180	180	2025-07-03 00:00	BIIB	132.860001	0	1	0	132.50	132.505005	130.100006	0.003777	1.844730	1	1	130.80	132.220001	132.940002	1.073969	1.609751	0	1	129.10	132.710007	133.274994	2.720222	3.132616	1	0	94. Should I keep small positions in LRCX, BIIB, and MNST or consolidate into one high-conviction pick?	Based on the analysis, we recommend consolidating into one high-conviction pick, LRCX, due to its strong growth prospects and relatively low volatility. The other two stocks, BIIB and MNST, have some concerns with their recent performance and may not be as attractive for investment.	True	False	False	Healthcare	1	1	2025-07-03
181	181	2025-07-03 00:00	MNST	63.080002	0	1	0	63.50	63.330002	62.860001	0.268432	1.018135	0	1	62.80	61.209999	59.540001	2.597616	5.475309	1	1	62.10	58.799999	58.480000	5.612246	6.190151	1	1	94. Should I keep small positions in LRCX, BIIB, and MNST or consolidate into one high-conviction pick?	Based on the analysis, we recommend consolidating into one high-conviction pick, LRCX, due to its strong growth prospects and relatively low volatility. The other two stocks, BIIB and MNST, have some concerns with their recent performance and may not be as attractive for investment.	True	True	True	Consumer	0	0	2025-07-03
182	182	2025-07-03 00:00	ASML	794.979980	0	0	1	555.12	783.830017	784.989990	29.178522	29.283175	0	0	562.15	793.205017	799.450012	29.129293	29.682908	1	0	573.21	796.640015	799.200012	28.046547	28.277028	1	1	95. What’s the ideal weight for a 4-stock mix of ASML, KLAC, INTU, and ROST?	{'ideal_weight': {'ASML': 30, 'KLAC': 25, 'INTU': 20, 'ROST': 25}, 'reasoning': 'This mix would provide a balanced portfolio with a mix of growth and stability, as well as exposure to different industries.'}	False	True	True	Technology	0	0	2025-07-03
183	183	2025-07-03 00:00	KLAC	924.659973	0	0	1	345.67	905.905029	912.900024	61.842579	62.134956	0	0	352.11	915.510010	923.299988	61.539470	61.863966	0	0	362.45	922.099976	929.145020	60.692982	60.991019	0	1	95. What’s the ideal weight for a 4-stock mix of ASML, KLAC, INTU, and ROST?	{'ideal_weight': {'ASML': 30, 'KLAC': 25, 'INTU': 20, 'ROST': 25}, 'reasoning': 'This mix would provide a balanced portfolio with a mix of growth and stability, as well as exposure to different industries.'}	False	False	True	Semiconductors	0	0	2025-07-03
184	184	2025-07-03 00:00	INTU	780.799988	0	0	1	245.98	778.840027	783.549988	68.417134	68.606981	0	1	251.23	767.010010	769.690002	67.245538	67.359586	0	0	258.51	743.929993	748.670105	65.250762	65.470773	0	0	95. What’s the ideal weight for a 4-stock mix of ASML, KLAC, INTU, and ROST?	{'ideal_weight': {'ASML': 30, 'KLAC': 25, 'INTU': 20, 'ROST': 25}, 'reasoning': 'This mix would provide a balanced portfolio with a mix of growth and stability, as well as exposure to different industries.'}	True	False	False	FinTech	0	0	2025-07-03
185	185	2025-07-03 00:00	ROST	131.649994	0	0	1	95.21	129.899994	130.770004	26.705154	27.192784	0	0	96.58	130.690002	131.210007	26.099933	26.392809	0	0	98.01	129.750000	129.860001	24.462428	24.526413	0	0	95. What’s the ideal weight for a 4-stock mix of ASML, KLAC, INTU, and ROST?	{'ideal_weight': {'ASML': 30, 'KLAC': 25, 'INTU': 20, 'ROST': 25}, 'reasoning': 'This mix would provide a balanced portfolio with a mix of growth and stability, as well as exposure to different industries.'}	False	False	False	Consumer	0	0	2025-07-03
186	186	2025-07-03 00:00	EBAY	76.389999	0	1	0	74.50	75.288696	75.889999	1.047563	1.831598	1	1	73.20	75.170799	76.279999	2.621762	4.037754	1	1	72.50	76.339996	76.339996	5.030124	5.030124	1	1	99. I’ve held eBay for a while, but I’m considering rotating into either clean energy or industrials. What’s better for 2025?	{'coming_day': 'Hold your position in EBAY for the next day as the stock is expected to recover slightly.', 'coming_3_days': 'Consider rotating out of EBAY in the next 3 days as the stock is expected to decline further.', 'coming_week': 'Rotate into clean energy or industrials for the long-term as they are expected to perform better in 2025.', 'reasoning': "The stock's recent underperformance and negative trend indicators suggest a decline in the short-term. However, the company's growth potential and positive news about Louis Vuitton and Birkenstock's performance on the platform suggest that it may be a good long-term investment. Clean energy and industrials are expected to perform better in 2025 due to their growth potential and increasing demand."}	True	True	True	Consumer	0	0	2025-07-03
187	187	2025-07-03 00:00	ADP	308.959991	0	0	0	313.00	310.725006	308.489990	0.732157	1.461963	0	0	316.00	308.612488	308.410004	2.393783	2.461008	0	0	320.00	305.070007	304.149994	4.893956	5.211247	0	0	100. I'm looking at ADP, NTES, and KLAC for long-term growth. Which two have better upside if I can only pick a pair?	Based on the analysis, I recommend investing in KLAC and NTES for long-term growth. KLAC has a strong financial performance and a high demand for its stock, while NTES is expanding its reach in the global music industry. ADP is a stable company, but it may not have the same level of growth as KLAC and NTES.	True	True	True	Technology	0	0	2025-07-03
188	188	2025-07-03 00:00	NTES	132.860001	1	0	0	137.00	134.154999	133.039993	2.120682	2.976554	1	1	140.00	131.050003	130.199997	6.829452	7.526884	0	0	144.00	128.860001	128.259995	11.749185	12.271952	0	0	100. I'm looking at ADP, NTES, and KLAC for long-term growth. Which two have better upside if I can only pick a pair?	Based on the analysis, I recommend investing in KLAC and NTES for long-term growth. KLAC has a strong financial performance and a high demand for its stock, while NTES is expanding its reach in the global music industry. ADP is a stable company, but it may not have the same level of growth as KLAC and NTES.	True	False	False	Technology	0	0	2025-07-03
189	189	2025-07-03 00:00	KLAC	924.659973	1	0	0	930.00	922.799988	912.900024	0.780235	1.873149	0	0	935.00	931.664917	923.299988	0.357970	1.267195	0	1	940.00	929.255005	929.145020	1.156302	1.168276	1	1	100. I'm looking at ADP, NTES, and KLAC for long-term growth. Which two have better upside if I can only pick a pair?	Based on the analysis, I recommend investing in KLAC and NTES for long-term growth. KLAC has a strong financial performance and a high demand for its stock, while NTES is expanding its reach in the global music industry. ADP is a stable company, but it may not have the same level of growth as KLAC and NTES.	False	False	True	Semiconductors	1	0	2025-07-03

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

Uppercase Letter

Lowercase Letter

Space Separator

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII